דרושים » דאטה » Senior Data Engineer

We are seeking a highly skilled and analytical Senior Data Engineer to join our Data team. In this role, you will design and implement robust data pipelines, while uniquely bridging the gap between engineering and analytics by actively analyzing data to extract actionable insights. You will play a crucial part in architecting our data foundations to support everything from business intelligence to advanced machine learning and agentic AI pipelines.
As a Senior Data Engineer, you will collaborate closely with engineering teams, product managers, and stakeholders across the organization. You will not only build the infrastructure utilizing modern data stack tools but also act as a data analyst when needed, ensuring our systems are fully equipped to operate within and support a cutting-edge agentic AI environment.
Responsibilities
Design, build, and maintain highly scalable ELT/ETL data pipelines.
Architect and manage modern cloud data warehousing solutions.
Develop, maintain, and monitor Python services responsible for robust data collection and ingestion.
Perform hands-on data analysis to interpret complex datasets, identify trends, and deliver business insights, acting in a dual capacity as a Data Analyst.
Develop and optimize data infrastructure specifically designed to support autonomous agentic workflows and LLM integrations.
Collaborate with engineers and analysts to troubleshoot data issues, enforce quality SLAs, and define data requirements.
Document data architecture, flow, and analytics standards for internal team alignment.
Build and maintain dashboards and reports to communicate analytical findings and data health to the organization.
Maintain Kafka consumer applications that process high-volume event streams in real-time, ensuring reliable ingestion into cloud databases.

we are looking for a Big Data Engineer.
As a Senior Big Data Engineer, working within Mobility Group, you will play a pivotal role in designing, developing, and maintaining the data infrastructure that powers our location analytics platform.
RESPONSIBILITIES:
Data Pipeline Architecture and Development: Design, build, and optimize robust and scalable data pipelines to process, transform, and integrate large volumes of data from various sources into our analytics platform.
Data Quality Assurance: Implement data validation, cleansing, and enrichment techniques to ensure high-quality and consistent data across the platform.
Performance Optimization: Identify performance bottlenecks and optimize data processing and storage mechanisms to enhance overall system performance and reduce latency.
Cloud Infrastructure: Work extensively with cloud-based technologies (GCP and AWS), to design and manage scalable data infrastructure.
Collaboration: Collaborate with cross-functional teams including Data Analysts, Data Scientists, Product Managers, and Software Engineers to understand requirements and deliver solutions that meet business needs.
Data Governance: Implement and enforce data governance practices, ensuring compliance with relevant regulations and best practices related to data privacy and security.
Monitoring and Maintenance: Monitor the health and performance of data pipelines, troubleshoot issues, and ensure high availability of data infrastructure.
Mentorship: Provide technical guidance and mentorship to junior data engineers, fostering a culture of learning and growth within the team.

Required Senior ML Data Engineer
About the team:
The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

Required Senior Software Engineer, Infrastructure, Cloud
About the job
Our software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to our needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
As a Platform Engineer on this team, you will build, operate, and scale the mission-critical cloud infrastructure that powers Chronicle Security Orchestration, Automation and Response (SOAR) at scale. This is a heavy infrastructure operations and automation role. You will take autonomous ownership of complex operational challenges-from administering advanced Kubernetes (GKE) clusters to building sophisticated observability pipelines-ensuring the highest level of Enterprise-grade reliability and security.
You will not simply implement others' ideas, you will be expected to scope operational challenges, write robust Infrastructure as Code (IaC), and transform manual toil into software-driven platforms in close partnership with product development teams.
Responsibilities
Own all aspects of your immediate infrastructure area, leading the design and maintenance of platform tooling, deployment pipelines, and cloud systems that enhance the reliability and performance of Chronicle SOAR.
Administer, operate, and troubleshoot advanced Kubernetes (GKE) clusters and onboard new infrastructure features at scale.
Develop and manage infrastructure configurations and automation policies using Go to ensure secure, consistent, and auditable management of GCP resources.
Design and implement sophisticated monitoring, logging, and tracing solutions (e.g., Prometheus, Grafana) and manage Service Level Objectives (SLOs)/Service Level Indicators (SLIs).
Act as a point of contact for cross-functional partners. Analyze past incidents and proactively develop software solutions to prevent recurrence. Build automation to reduce manual toil and accelerate incident resolution.

we are looking for a Senior AI Software Engineer.
As a Senior AI Software Engineer, you'll help build that platform end to end - the agents, connectors, automation, and infrastructure that let the entire company unlock major productivity gains. You'll be designing and shipping AI that takes real work off people's plates, turning manual processes into automated ones, and building the systems that let teams do their best work faster.
Developing our use of Claude and Databricks - currently the backbone of that internal AI stack - is a key part of the mandate. Every one of our 500+ team members already uses Claude and BI in Databricks for analysis, automation, and research, and demand for deeper integrations is growing faster than our R&D team can deliver. You'll be tasked with driving these capabilities and impact to the next level - from surface-level usage to genuinely agentic, high-leverage workflows embedded across the business.
This is a hybrid role: part platform engineer, part internal-facing integration lead. You'll report to the COO and partner across AI Operations, R&D, Data Science, GTM, and other teams, owning the path from integration request to production-grade system. You'll also help build the triage and review process so the broader org can self-serve safely.
If you want to build the AI backbone of a fast-moving company - and see your work adopted by hundreds of people within days rather than quarters - this is that role.

We're looking for a Senior AI Engineer to design, build, and ship production-grade LLM agents that reason over workforce and skills data on top of Loomra's semantic layer. You won't just prototype - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily (Teams, Slack, Copilot), and iteration in front of enterprise customers. You'll work alongside product, data, and platform engineers to turn powerful capabilities into reliable, safe, and fast product experiences.
If you've built agents that actually made it to production - and you care as much about evaluation, guardrails, and reliability as you do about capability - we want to talk to you.
Responsibilities
Design and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
Implement retrieval and RAG pipelines over structured and unstructured workforce data, grounded in our knowledge graph connecting people, jobs, and skills.
Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
Build evaluation harnesses, guardrails, and safety/bias checks, and work within the governance engine so agents behave reliably, respect customer policies, and produce a full audit trail.
Ship agents in a model-agnostic way across providers (Anthropic, Google, IBM watsonx) and deploy them into Teams, Slack, and Copilot.
Optimize agents for latency, cost, and reliability at enterprise scale.
Take agents from prototype to production - with monitoring, observability, and a fast iteration loop.
Partner closely with product, data, and platform teams to translate customer needs into agent capabilities.

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind our company's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team.
What your team owns
The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains:
1. Content-moderation inference at scale
Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.
2. GenAI safety & red-teaming
Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming our company's leading edge into agentic-AI safety.
3. Data platform & MLOps
An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection.
What you'll do
Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders.
Leadership competencies
People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.

We are looking for a Backend / Data Engineer to join our Core Data Pipeline team. In this role, you will be responsible for building and scaling the engine that processes our high-throughput, real-time telemetry data.
Our pipeline handles billions of events per second, performing real-time processing, data enrichment, and other transformations. If you thrive on solving high-scale, low-latency distributed systems challenges - this is the place for you.
What Youll Do:
High-Scale Engineering: Design, build, and maintain our central data processing pipeline, handling massive volumes of logs and traces at extreme scale.
Transform & Enrich: Architect real-time stream processing systems to perform complex data transformations and enrichments in real time.
Performance & Efficiency: Optimize system throughput, reduce memory footprints, and minimize latency across distributed clusters.
Architecture & Reliability: Take full ownership of feature lifecycles - from design and system architecture to production monitoring and resilience.
Our Stack:
Scala, Rust, Node.js
Kafka
Kafka Streams / Akka Streams
ClickHouse, Redis
Kubernetes
AWS.

We are seeking a Senior Software Engineer with a strong foundation in backend engineering and experience building scalable, high-performance systems.
This role focuses on designing and developing backend services that power our **AI-driven products**, with extensive work on **Large Language Models (LLMs), AI agents, and modern GenAI technologies**.
We foster a professional environment where experienced engineers collaborate to drive technical excellence and continuously improve our architecture and products. As a Senior Software Engineer, you will play a key role in building production-grade AI systems, solving complex engineering challenges around scalability, performance, reliability, and AI orchestration.
Role & Responsibilities:
* Design, develop, and optimize scalable backend services and APIs that power AI-driven products.
* Build production-grade applications and workflows using **LLMs, AI agents, and GenAI technologies**.
* Design and implement agentic workflows, including **tool calling, orchestration, context management, and integration with internal services**.
* Integrate and optimize LLM-based capabilities while balancing **quality, latency, reliability, and cost**.
* Design and maintain scalable, reliable, and high-performance distributed systems.
* Take ownership of technical solutions from design and implementation through deployment and production monitoring.
* Improve system architecture, observability, code quality, and development processes.
* Collaborate with Product, DevOps, and other engineering teams to deliver end-to-end solutions.
* Stay up to date with advancements in backend technologies and the rapidly evolving GenAI ecosystem.

We are looking for a Software Engineer to help design and develop the core services and applications that drive our data capabilities. In this role, you will build high-quality, scalable software; contribute to the architecture of our next-generation data systems; and collaborate with research and product teams to turn cutting-edge ideas into production-ready features.
Responsibilities:
Design, develop, and maintain scalable backend services and components that power our data workflows, analytics, and product features.
Build high-quality internal tools and applications that improve engineering workflows and enable data-driven development across the organization.
Contribute to the architecture and evolution of our new data platform with a strong focus on clean design, testability, maintainability, and performance.
Collaborate closely with security researchers, analysts, and product managers to translate innovative cybersecurity concepts into reliable, production-ready software.
Apply engineering best practices across the stack - including code quality, testing, observability, versioning, and documentation - to ensure system robustness as we scale.